Nonlinear measure approach for the robust exponential stability analysis of interval inertial Cohen–Grossberg neural networks

This article is concerned with the existence and robust stability of an equilibrium point that related to interval inertial Cohen–Grossberg neural networks. Such condition requires the existence of an equilibrium point to a given system, so the existence and uniqueness of the equilibrium point are emerged via nonlinear measure method. Furthermore, with the help of Halanay inequality lemma, differential mean value theorem as well as inequality technique, several sufficient criteria are derived to ascertain the robust stability of the equilibrium point for the addressed system. The results obtained in this article will be shown to be new and they can be considered alternative results to previously results. Finally, the effectiveness and computational issues of the two models for the analysis are discussed by two examples. © 2016 Wiley Periodicals, Inc. Complexity 21: 459–469, 2016     

Fast search algorithms for computational protein design

One of the main challenges in computational protein design (CPD) is the huge size of the protein sequence and conformational space that has to be computationally explored. Recently, we showed that state‐of‐the‐art combinatorial optimization technologies based on Cost Function Network (CFN) processing allow speeding up provable rigid backbone protein design methods by several orders of magnitudes. Building up on this, we improved and injected CFN technology into the well‐established CPD package Osprey to allow all Osprey CPD algorithms to benefit from associated speedups. Because Osprey fundamentally relies on the ability of to produce conformations in increasing order of energy, we defined new strategies combining CFN lower bounds, with new side‐chain positioning‐based branching scheme. Beyond the speedups obtained in the new ‐CFN combination, this novel branching scheme enables a much faster enumeration of suboptimal sequences, far beyond what is reachable without it. Together with the immediate and important speedups provided by CFN technology, these developments directly benefit to all the algorithms that previously relied on the DEE/ combination inside Osprey* and make it possible to solve larger CPD problems with provable algorithms. © 2016 Wiley Periodicals, Inc.     

Neural network and ReaxFF comparison for Au properties

We have studied how ReaxFF and Behler–Parrinello neural network (BPNN) atomistic potentials should be trained to be accurate and tractable across multiple structural regimes of Au as a representative example of a single‐component material. We trained these potentials using subsets of 9,972 Kohn‐Sham density functional theory calculations and then validated their predictions against the untrained data. Our best ReaxFF potential was trained from 848 data points and could reliably predict surface and bulk data; however, it was substantially less accurate for molecular clusters of 126 atoms or fewer. Training the ReaxFF potential to more data also resulted in overfitting and lower accuracy. In contrast, BPNN could be fit to 9,734 calculations, and this potential performed comparably or better than ReaxFF across all regimes. However, the BPNN potential in this implementation brings significantly higher computational cost. © 2016 Wiley Periodicals, Inc.     

具有随机扰动和多重变时滞的神经网络稳定性分析

考虑了一个具有多重时变时滞的随机神经网络的全局渐近稳定性问题.通过构造Lyapunov-Krasovskii函数并运用广义Ito公式,得到了一个充分条件,条件保证了神经网络在随机扰动下的全局均方渐近稳定性.最后通过一个数值实例验证了结果的有效性.     

Continuous-Time Independent Edge-Markovian Random Graph Process

In this paper, the continuous-time independent edge-Markovian random graph process model is constructed. The authors also define the interval isolated nodes of the random graph process, study the distribution sequence of the number of isolated nodes and the probability of having no isolated nodes when the initial distribution of the random graph process is stationary distribution, derive the lower limit of the probability in which two arbitrary nodes are connected and the random graph is also connected, and prove that the random graph is almost everywhere connected when the number of nodes is sufficiently large.     

Existence and global exponential stability of almost periodic solution for delayed competitive neural networks with discontinuous activations

In this paper, we study a class of delayed competitive neural networks with discontinuous activations. Without assuming the boundedness and local Lipschizian on the activation functions, some new criteria ensuring the existence and global exponential stability of almost periodic solutions for the neural network model considered in this work are established by constructing some suitable Lyapunov functionals and employing the theory of nonsmooth analysis. Finally, we present some applications and numerical examples with simulations to show the effectiveness of our main results. Copyright © 2016 John Wiley & Sons, Ltd.     

Directed neural stem cell differentiation on polyaniline-coated high strength hydrogels

Stem-cell-based neural regeneration has received significant attention, as it has potential to restore functionality to diseased or damaged neural tissues that have a limited ability to self-repair or regenerate. Culturing neural stem cells (NSCs) on hydrogel substrates has been shown to facilitate differentiation to neural progenitors, but this has only been achieved on very soft hydrogels, greatly increasing the difficulty of manufacture and limiting their wide applications. Here, we realized the differentiation of NSCs to neural and glial progenitors on high-strength hydrogels. Hydrogen-bonding-strengthened conductive hydrogels (PVV-PANI) were synthesized through one-pot copolymerization of 2-vinyl-4,6-diamino-1,3,5-triazine, 1-vinylimidazole and polyethylene glycol diacrylate, followed by post-coating with polyaniline (PANI). Diaminotriazine-diaminotriazine hydrogen bonding dramatically increases their mechanical strength, while copolymerization with VI pronouncedly promotes the adsorption of PANI particles, endowing the hydrogels with electrical conductivity. These hydrogels exhibit tensile strengths up to 1.16 MPa, a 559% breaking strain, a 9.9 MPa compressive strength and up to 16.7 mS/cm conductivity. Importantly, PVV-PANI hydrogels support the attachment, proliferation, and differentiation of NSCs, and allow the efficient induction of neural and glial differentiation via electrical stimulation. This work demonstrates high-strength conductive hydrogels can serve as an electroactive soft-wet platform for modulating the specific differentiation of NSCs, a significant step towards cell-based therapies for neurological diseases.     

Quiz games as a model for information hiding

We present a general computation model inspired in the notion of information hiding in software engineering. This model has the form of a game which we call quiz game. It allows in a uniform way to prove exponential lower bounds for several complexity problems.